Identification and characterization of a novel interaction between peroxisome-proliferator activated receptor gamma (PPARγ) and human mesoderm induction-early response 1 and potential implications for adipogenesis
Bibliographic record
Abstract
1222 PPARγ is a nuclear hormone receptor and master regulator of lipid metabolism and adipogenesis. It is also the target for the thiazolidinediones, a class of drugs used in the treatment of type 2 diabetes. PPARγ regulates these processes through the recruitment of a diverse set of transcriptional coregulators (both coactivators and corepressors) in a tissue and time specific manner. This study focused on characterizing the interaction between PPARγ and human mesoderm induction early response 1 (hMI-ER1), a transcription factor that has been shown to interact with other nuclear hormone receptors and regulate target gene expression. Glutathione-S-transferase pull-down assays have revealed that PPARγ interacts with both the α and β forms of hMI-ER1, specifically through the SANT domain located near the C-termini of both protein isoforms. Coimmunoprecipitations in HEK-293 (human embryonic kidney cells) confirmed that this interaction occurs in vivo . Treatment with ligand (troglitazone) had no effect on the ability of PPAR to bind hMI-ER1 indicating that the interaction is ligand-independent. A reporter assay using luciferase regulated by the PPAR response element (PPRE) demonstrated that hMI-ER1α and β cause a 2-fold activation of PPAR-driven transcriptional activity and this was similar to the 3-fold activation observed with a known PPARγ coactivator (PPARγ coactivator 1-alpha, PGC1-α). This activation was also ligand-independent. Thus, hMI-ER1 interacts with PPAR in a ligand independent manner through its SANT domain, and causes activation of PPARγ-mediated transcriptional activity. We have recently discovered that hMI-ER1 expression is regulated in 3T3-L1 preadipocytes during their differentiation into adipocytes. Future work will determine the role of hMI-ER1 in adipogenesis using the well-established 3T3-L1 differentiation system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".